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Contract No. DE-AC36-08GO28308

Minimizing Wind Power 

Producer’s Balancing Costs 

Using Electrochemical Energy 

Storage

Preprint 

J. Miettinen,

1,2 V. Tikka,2

 J. Lassila,2 

J. Partanen,2 and B.-M. Hodge3

1 VTT Finland

2 LUT Energy

3 National Renewable Energy Laboratory 

To be presented at the Nordic Conference on Electricity Distribution 

Management and Development

Stockholm, Sweden

September 8–9, 2014 

Conference Paper

NREL/CP-5D00-62191

August 2014 
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MINIMIZING WIND POWER PRODUCER’S BALANCING COSTS USING 

ELECTROCHEMICAL ENERGY STORAGE

Jari Miettinen (VTT/LUT), Ville Tikka (LUT), Jukka Lassila (LUT), 

Jarmo Partanen (LUT), Bri-Mathias Hodge (NREL) 

SUMMARY

In this paper the question of how the electrochemical energy storage can be used to decrease 

the balancing costs of a wind power producer in the Nordic market is studied. Since the 

electrochemical energy storage is developing in both technological and financial terms, a

sensitivity analysis was carried out for the most important variables the wind-storage hybrid 

system. The system was studied from a wind power producer’s point of view. The main result 

is that there are no technical limitations to using storage for reducing the balancing costs. 

However, in terms of economic feasibility, installing hybrid wind-storage systems like the one 

studied in this paper faces challenges in both the short and long terms. 

One of the stated benefits of electrochemical batteries is said the ability to also help in

integrating wind power into the power system. However, using storage to even out the 

uncertainty of day-ahead forecasts can only provide 50% of the balancing energy with a 1 

MW energy storage system and a 12 MW storage system is required to balance 75 %, with 

higher associated investment costs. 

KEYWORDS

Wind power, Energy storage, Wind power forecasting, Balancing, Batteries, Battery Systems
2 

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1. INTRODUCTION

Wind power has a variable and uncertain nature, which causes difficulties in predicting power output

in the day-ahead horizon. In the Nordic market wind power participates in the market together with 

conventional generation. Therefore, wind power producers have to deal with their own imbalanced 

energy from inaccurate forecasts. Thus, their share of imbalance costs is much larger than for 

controllable conventional generation. One way to decrease balancing costs is to install electrochemical 

storage next to a wind turbine site, the purpose of which is to reduce the amount of imbalance energy. 

In this paper this kind of hybrid wind-storage setup is studied and its economic feasibility is examined. 

2. MARKET MODEL

The studied area in this paper is a site from Finland and thus the market model and rules from Nord 

Pool are used. In Nord Pool there are two places to trade physical electricity delivery, day-ahead and 

intraday markets. In the day-ahead market the bids for the coming day’s delivery hours must be 

submitted before 1 pm Finnish time [1]. The area prices are then determined based on the crossing 

point of demand and supply curves while considering the constraints in the transmission system. The 

intraday trading begins immediately after the day-ahead prices are announced for the delivery hours. 

However, in this paper it is assumed that the producer will not trade in the intraday market and only 

participates in the day-ahead market. The intraday market is not used for two reasons: a) the intraday 

market is not a liquid marketplace b) trading in the intraday market might not be beneficial, since the

two price model is used for production balances [2]. 

In the Nordic power market producers’ imbalances are punished with balancing prices, which are

connected to the regulation prices. The regulation prices are formulated by the need for system 

regulation in the whole Nordic system, with up and down regulation separately determined. When a 

delivery hour has a surplus of energy down regulation bids must be activated in order to stabilize the 

system and down regulation prices are used to penalize producers’ surplus imbalances. At the same 

time producers who have a deficit from the contracted energy delivery can deal with their imbalances 

at the day-ahead price. On contrary, when a delivery hour has a deficit of energy, up regulation bids 

must be activated and producers with deficit of energy must pay the up regulation price.

However, when there are transmission constraints between different price regions the local 

transmission system operator (TSO) is responsible for balancing the production in its area, instead of 

balancing the interconnected area as a whole.

2.1 Mathematical formulation of Nordic market

The revenue, ρk in the Nordic market can be formulated as the sum of maximum possible income 

from the spot market and the cost of imbalance energy, equation (1)

ρk = πk

(S)

Wk + Ck

(↑/↓) (1)

where πk

(S) is the day-ahead price for hour k, Wk is the produced energy, Ck

(↑/↓) is the cost imbalance 

for hour k. The imbalance cost term Ck

(↑/↓) can be written as

Ck

(↑/↓) = ψk

(↓)

� Wk − W�k

(S)

�, Wk ≥ W�k

(S)

ψk

(↑)

� Wk − W�k

(S)

�, Wk ≤ W�k

(S)

(2) 

where unit balancing costs for down and up regulation, ψk

(↓) and ψk(↑) can be formulated as
3 

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ψk

(↓) = πk(↓) − πk(S)

ψk

(↑) = πk(S) − πk(↑) (3) 

By considering how the market operates, unit balancing costs are always zero or negative. 

3. BATTTERY MODEL

For analysing the state and wear-and-tear of an electrochemical battery, a simplified model of an

electrochemical battery is used. The parameters included in the model are: cycle life, maximum 

operating life, maximum capacity, unit investment cost, and cycle efficiency. Cycle life of a battery is 

estimated by using the rainflow-algorithm [3], which is usually used to analyse fatigue on materials 

when the stress is varying. The rainflow-algorithm can provide an estimate of the frequency of 

different sizes of cycles that the battery goes through in its operational periods. 

In this paper the relationship between cycle life and depth of charge is assumed to be linear, which 

applies for instance for LiFePo-tehcnology when the battery is operated in a constant environment [4]. 

Thus, it is assumed that the energy that the battery can charge/discharge in its lifetime is assumed to be 

constant. In order to know how many full cycles the battery goes through in the operational period, the 

so called equivalent cycles, Cekv is calculated. When calculating equivalent cycles of the battery, all 

cycles below the battery’s nominal value must be weighted by a energy dependent weight, equation 

(4) 

Cekv = � Camp

i

Cmax

Nmax

B

i=Nmin

B

∙ i (4) 

where Nmin

B and Nmax

B are the number of cycles that the battery does in its minimum and maximum 

cycle amplitude, respectively. The amplitude of a cycle is Camp

i and the maximum capacity of the

battery is Cmax. Maximum operation time, Lop of the battery is the relation of maximum cycle life to

the yearly equivalent cycles that the battery goes in a year. However, the maximum technological 

lifetime of a battery is assumed to be limited for cases when Lop exceeds maximum lifetime of a 

battery Lop

max. 

Lop = Cmax

Cekv/8760

(5) 

The annuity of an investment, I is calculated by using equation (6) 

Iann = i(1 + i)Lop

(1 + i)Lop − 1 ∙ I (6) 

The unit capital cost of an investment is the annuity of an investment divided by the energy used, Ebat

in the battery, equation (7) 

Ucap = Iann

Ebat

(7) 

The capacity of a battery, C can never go under zero and it cannot exceed its nominal capacity, such

that C ∈ [0, Cmax]. 
4 

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3.1 Battery Charging

For battery charging the prediction error, εk, must be positive. However, when charging there are 

losses due to the efficiency, δ, of the battery. Thus, the actual stored energy is: 

ε̂

k

store = δεk (8)

When energy is lost, which would have been otherwise sold to the market, there is a cost ρloss, which 

is proportional to the down regulation price in that hour. However, charging prediction errors gives the 

possibility to save on balancing costs, ρavoid. 

ρk

store = ρkavoid − ρkloss = ψk

(↓)

εk − πk

(↓)

ε̂ (9)

When there is a situation that the stored energy would exceed the maximum capacity of the battery, 

that portion of energy must be sold to the market as there would not be any available storage capacity. 

3.2 Withdrawing energy

When the prediction error is negative, energy must be withdrawn from the battery in order to keep the 

production as close to the contracted as possible. However, when energy is withdrawn from the 

battery, the efficiency of the battery must be considered. Therefore, more energy must be taken from 

the battery to cover the prediction error.

ε̂= εk

δ

(10) 

The avoided costs are then: 

ρk

withd = εkψk

(↑) (11)

When taking the energy from the battery there is no need to calculate the efficiency losses, as it is 

considered only when charging the battery.

3.3 Energy cost

The unit benefit of stored energy to the battery is dependent on the capital cost of the battery and the 

avoided costs from the balancing market. Unit benefit is the sum of avoided costs, ρk

avoid in relation to 

used energy in the battery, equation 12. 

Uben = ∑ ρk

8760 avoid

k=1

Ebat

= ∑ ρk

store + ρk8760 withd

k=1

Ebat

(12) 

Therefore, the average unit cost of energy at the battery is: 

Ucost = Uben − Ucap (13)
5 

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4. FORECAST ERRORS

It is important to understand the basic statistical properties of prediction errors when considering the 

impacts on storage. The distribution of prediction errors can be considered as hyperbolically

distributed [5]. An international comparison of forecasting error distributions was carried out in [6]

where different characteristics of forecasting errors were studied. In figure 1 an autocorrelation plot 

and a histogram of prediction errors with hyperbolic fit is presented. The prediction errors are strongly 

autocorrelated, which means that energy is usually pushed to the battery or withdrawn from the battery 

for multiple consecutive hours at a time. This can cause the capacity limits of the storage to be easily 

reached and thus the economy of the wind-storage system is reduced.

5. SIMULATIONS

In order to study the economics of electricity storage, a sensitivity analysis was carried out to see how 

the average unit costs of energy (equation (13)) changes by varying some of the important parameters

of the wind-storage system. Since the development of the forecasting techniques has been fast for the 

past decade, two different sensitivity scenarios were simulated: forecast error accuracy was improved 

uniformly by 10% and 50%. Besides uniform improvement to prediction errors, artificial prediction 

error time series were created without any autocorrelative properties to see the significance of 

autocorrelation in prediction errors. Besides the forecasting error accuracy, balancing prices are the 

other major driver why the installation of battery would be a profitable investment. However, the 

development of future balancing prices is not so straightforward to forecast and therefore two different 

scenarios were applied, a high and a low price scenario. In the high price scenario the up and down 

unit balancing costs were increased by multiplying each unit balancing cost with a factor of two. For

the low price scenario the prices were halved. It is assumed that the spot-prices are reduced and the 

balancing prices remained the same, which means that the efficiency losses are dealt with at the 

current balancing prices.

Since development in battery technologies is ongoing and taking big leaps in terms of cost-benefit all 

the time, multiple different electrochemical storage related variables were varied in order to have a 

good understanding of how the development of batteries will affect the benefits of the wind-storage 

system. The varied parameters are cycle life, investment unit cost, and capacity of the battery. The 

future values of the parameters are based on the U.S. Department of Energy views for long term 

storage technology development [7]. 

Table 1 Modified parameters in the simulation

starting value end value

Cycle life [cycles] 2000 6000

Investment cost [€/kW] 1300 700

Capacity of battery [MW] 0.5 12

Figure 1 Autocorrelation plot and prediction error histogram with hyperbolic fit
6 

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There are parameters that are assumed to be constant in the simulation, as seen in table 2. 

Table 2 Constant parameters in the simulation

variable value

interest rate, i 5 %

nominal capacity of wind turbine site, Pn 30 MW

round trip efficiency, δ2 85 %

maximum battery life, Lop

max 20 years

5.1 Investment price sensitivity

In the base scenario two different unit investment prices were looked into: long-term unit investment

price, 700 €/kWh and a near-term price, 1300 €/kWh. The simulations are run with the current 

forecasting error levels and with the current balancing prices, and the results are shown in figure 2.

Figure 2 Unit cost of energy for two unit investment prices 700 €/kWh (left) and 1300 €/kWh (right)

The unit cost of energy is strongly negative, which means that the capital costs of the battery are much

greater than the avoided costs. The profitability is always increasing, or remaining as same, when the 

cycle life is increased. The optimal size of battery is around 4MWh for a 700 €/kW unit investment 

price and for a 1300 €/MWh price it is 3 MWh. The profitability of the battery is clearly increased as 

the investment price is reduced. 

5.2 Forecast error sensitivity

In this case different scenarios are simulated with the near term investment price scenario, 1300 

€/kWh and see how the improvement in the forecast error accuracy will impact the profitability of a 

battery investment, as seen in figure 3. 

Figure 3 Unit cost of energy for two different forecasting error improvement scenarios: 10% (left) and 50 

% (right)
7 

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As the forecast models’ develop it will have an effect on favouring smaller energy storages since the 

reduction of large prediction errors will reduce the benefit of having a large storage capacity at the 

wind turbine site. 

5.2.1 Removing autocorrelation 

When removing the autocorrelation of prediction errors the profitability of wind-storage system does 

not change much when comparing to the base case scenario in the chapter 5.1. The profitability of 

high capacity storages are however lowered probably because the battery does not need to be used as 

often. 

Figure 4 Unit cost of energy for two unit investment prices 700 €/kWh (left) and 1300 €/kWh (right) when 

the prediction errors are completely random

5.3 Sensitivity on unit balancing costs

Increasing unit balancing costs can bring additional value to the costs savings provided by the battery. 

However, if battery cannot store that energy it will show as an increased additional balancing cost. 

Therefore, the cost benefit of larger batteries will increase as the unit balancing costs are increased

since larger storage size can have higher additional savings, as seen in figure 4. However, when 

lowering the unit balancing prices the benefit of large scale energy storages is reduced. 

Figure 5 Unit cost of energy for two different unit balancing cost scenarios: 50% (left) and 200 % (right)

The autocorrelation of prediction errors has an impact on how efficiently it is to utilize a wind-storage 

system. In figure 5 we see the amount of non-balanced energy for different storage capacity sizes. It is 

important to notice that even having 12 MWh of storage, it is only possible to balance 65% of the 

imbalance energy. 
8 

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Figure 6 Non-balanced energy on different storage sizes

6. CONCLUSION

Using electro-chemical storage to balance wind power variability is an idea that would help producers 

to avoid balancing costs and also help the integration of variable generation to the power system. In 

this paper it was shown that there is currently no financial incentive to balance wind generation with 

electrochemical storage. It was also shown that even looking at the future expected investment prices 

of electrochemical storages and optimistic views of how battery technology will develop, the 

investment is far from profitable. The main reasons are the decreasing forecasting errors and relatively 

low unit balancing costs. Biased forecasts are also a big contributor to the economics of a windstorage system. From the perspective of minimizing the total system variability and increasing the 

profits of single producers, it is better to have well dispersed wind turbine sites and pool the 

production since the correlation of prediction errors when well dispersed is very low [8]. 

7. ACKNOWLEDGEMNTS

This work was carried out in the Smart Grids and Energy Markets (SGEM) research program 

coordinated by CLEEN Ltd. with funding from the Finnish Funding Agency for Technology and 

Innovation, Tekes. 

BIBLIOGRAPHY

[1] NordPoolSpot, “Nord Pool Spot,” 15 5 2014. [Online]. Available: http://www.nordpoolspot.com/How-does-it-work/Dayahead-market-Elspot-/. [Accessed 15 5 2014].

[2] H. Holttinen and G. Koreneff, “Imbalance Costs of Wind Power for a Hydro Power Producer in Finland,” WIND 

ENGINEERING, vol. 36, no. 1, pp. 53-68, 2012. 

[3] C. Amzallag, J. P. Gerey , J. L. Robert and J. Bahuaud, “Standardization of the rainflow counting method for fatigue 

analysis,” Journal of Fatigue, vol. 16, no. 4, pp. 287-293, 1994. 

[4] J. Wang, P. Liu, E. Sherman, J. Hicks-Garner, S. Soukiazian, M. Verbrugge , H. Tataria, J. Musser and P. Finamore, 

“Cycle-life model for graphite-LiFePO cells,” Power Sources, vol. 196, no. 8, pp. 3942-3948, 2011. 

[5] B.-M. Hodge, D. Lew, M. Milligan, H. Holttinen, S. Sillanpää, E. Gómez-Lázaro, R. Scharff, L. Söder, G. Larsén and G. 

Giebel, “Wind Power Forecasting Error Distributions: An International Comparison,” in Proceedings of 11th 

International Workshop on Large-Scale Integration of Wind Power into Power Systems, Lisbon, 2012. 

[6] J. Zhang, B.-M. Hodge, J. Miettinen, H. Holttinen, E. Gomez-Lazaro, N. Cutululis, M. Litong-Palima, P. Sørensen, A. 

Lovholm, E. Berge and J. Dobschinski, “Analysis of Variability and Uncertainty in Wind Power Forecasting: An 

International Comparison,” in Proceedings of 12th International Workshop on Large-Scale Integration of Wind Power 

into Power Systems as well as on Transmission Networks for Offshore Wind Farms, WIW2013, London, 2013. 

[7] U.S. Deparment of Energy, “Grid Energy Storage,” U.S. Department of Energy, 2013.

[8] J. Miettinen and H. Holttinen, “Prediction Errors and Balancing Costs in Finland Using Short Term Prediction for 

Different Geographical Areas,” in Proceedings of 12th International Workshopon Large-Scale Integration of Wind 

Powerinto Power Systems as well as on Transmission Networks for Offshore Wind Farms, WIW2013, London, 2013. 

 

0 2 4 6 8 10 12 0.34

0.36

0.38

0.4

0.42

0.44

0.46

0.48

0.5

0.52

capacity [MW]

relative non balanced energy

investment cost 700 €/kW